Why Automotive Needs to Get More Personal

The best salespeople in any industry have something in common: They get to know you first. They don’t ask you, “What are you interested in buying?” when you walk in the door. It doesn’t matter whether they’re trying to sell you a car, a laptop, or a sofa — they first seek to understand your lifestyle preferences and needs.

But this kind of personalization is not the norm in automotive.

Too often we’re leading the conversation with customers by talking about the product, not the person who is about to make the second-most expensive purchase of the life. For example, even though seven out of 10 consumers are undecided about make and model when they shop for a new car, nearly all online car search experiences force people to select make or model as the initial step in their journey — instead of first learning about the shopper and offering intelligent suggestions based on those learnings.

No wonder automotive shoppers would rather go to the DMV, clean toilets, have an extended phone conversation with their mother-in-law, or get stuck on jury duty than shop for a car.

Automotive Is Behind

The fact is, automotive has fallen behind other industries. On any given day, the shopper who visits a dealer’s lot or website is used to getting the kind of personalization that sites such as Netflix and Spotify provide. Industries ranging from online dating to fashion retail have become more personalized by applying machine learning to understand customers’ wants and needs and providing smarter product recommendations based on their likes and dislikes. They find out customer preferences first.

These sites are becoming more personalized by applying machine learning. Machine learning is a form of artificial intelligence in which computers train themselves to make smarter decisions. The self-learning comes from reading vast amounts of data (usually too vast for people to analyze quickly and accurately). With machine learning, a site goes beyond making superficial product recommendations based on your purchasing behavior and the purchasing habits of people like you. Sites learn from your likes and dislikes and from your lifestyle interests and behaviors to make personalized recommendations that you might not have thought of yourself.

Shoppers have had their expectations raised by personalization and will respond favorably when their expectations are met. Not surprisingly, 76 percent of consumers surveyed by Cars.com said they purchase products based on personalized recommendations either half or most of the time.

How Automotive Can Catch Up

To catch up, automotive brands need to look at how the leaders outside of the industry are using tools such as machine learning to create more relevant experiences. For example:

Spotify and Netflix famously apply machine learning to recommend songs and movies based on customers’ preferences matched against the interests of other customers with similar tastes. Spotify sifts through listening data – both yours and the people you follow – to recommend playlists that create true music discovery rather than simply replicate what you’ve been listening to already.

Dating site Tinder matches people with other people by first asking members to set up personal profiles and then suggesting matches to them. Tinder refines recommendations based on each person’s feedback.

Embracing machine learning is the kind of leap that more automotive sites need to make. Machine learning can make an automotive website offer smarter, more personal product recommendations to each shopper based on their browsing behavior and information that shoppers are willing to share about their personal lifestyles (e.g., whether they commute, love music, or live in an urban area). And with machine learning, a site can make recommendations that might not have been obvious to the shopper just like Spotify suggests an artist you might not have heard of but who is close enough to your tastes to interest you. It can also help determine the best match of a salesperson at the dealership. Or a dealership’s search and retargeting investments can become more personalized as your machine-learning-enabled CRM tools comb vast sets of user data.

Cars.com Matchmaking

At Cars.com, we’re taking our own advice. We recently launched a fundamental change to our site that re-imagines the car shopping experience. The new Cars.com Matchmaking Experience uses machine learning to give consumers personalized vehicle recommendations based on their lifestyle preferences. The more Cars.com learns about a person’s interests, the smarter and more personal the recommendations become.

Our site now gives site visitors the option to create personal profiles that build upon their lifestyle interests and needs. Maybe you’re a daily commuter who prefers style and comfort and prefers smartphone connectivity, or a sun lover who likes to hit the beach. We help you create a precise profile either way. From there, the site takes user preferences, combined with our 20 years of vehicle and consumer data, as well as sentiment analysis, to give shoppers a targeted list of cars.

Site visitors can swipe left or right to dismiss or favor the choices we provide. Based on shoppers’ choices, the site applies a proprietary machine learning algorithm to make smarter recommendations until the shopper finds the car of their dreams.

Our understanding of how all shoppers browse our site makes it possible for us to suggest vehicles that are related to a shopper’s preferences even if the suggestion is not a direct hit – just to give shoppers some unexpected ideas. For example, we might suggest a particular sedan to someone who selects SUVs as their ideal match if our data suggests that SUV lovers have also searched for a specific make and model of a large sedan.

A Personalization Strategy

Matchmaking is central to Cars.com’s strategy to transform car shopping and selling through personalization. Within the last year, for example, Cars.com also launched:

Salesperson Connect™, a feature that connects shoppers to a salesperson before ever visiting a dealership.

Hot Car, which uses a uses a proprietary machine learning algorithm to identify which vehicles on Cars.com are most likely to sell quickly.

With Matchmaking, we’re delivering to dealerships not only more qualified leads on the lot but a more engaged customer online. A pilot of Matchmaking Experience has resulted in a 752 percent increase in profile creation on the site, 87 percent increase in return visitors, 225 percent increase in email leads, and two times the number of page views per visitor versus the traditional search experience.

We’re just beginning to tap into the power of machine learning and artificial intelligence. What we consider personal today will likely be superseded by an even better experience as machine learning evolves. Now is the time to apply a time-honored sales best practice and really get personal with your customers.

On this week’s episode of Kain & Co., host and President of Kain Automotive, David Kain talks about adapting the language your BDC uses during...

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